1. Getting Down to Business
DAMA Chicago
Schaumburg, IL
June 21, 2017
Getting Down to Business
Integrating the Data Lake Within an Analytics and
Business Intelligence Ecosystem
Susan Swanson
Health Care Service Corporation (HCSC)

2. Agenda Introduction and Background
Requirements Gathering and Implementation
Communications
Proving It Out
Controlled Self-Service Business Intelligence
Summary and Conclusion
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3. By the Numbers
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4. Today’s Health Care Insurance Landscape
An evolving health insurance environment
Focusing on quality, cost effective health care
Addressing affordability of care
Managing health care outcomes
Tracking health care costs
Using technology and data to help navigate the health care system
Leveraging data with predictive analytics and social network analysis
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5. Data Lake Within the Data Ecosystem - Data Architecture Layers
Raw
Common Format
Consuming and Sandbox
Metadata
Data Governance
Hadoop Data Lake
Landing
Enterprise
Application
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6. Requirements Gathering and Implementation
Partnering with business data stewards and analytics and reporting resources guides delivery to focus on desired benefits and outcomes.
Elicitation 1
Validation and Acceptance
“The quality of the product, and the benefit it brings, will be remembered long after the project budget and deadlines have been forgotten about.” Adrian Reed 
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7. Requirements - Elicitation
Capture Capabilities and Needs
Provided at a high-level from the business
Tied to business goals and desired outcomes
Drill Down to Detailed Requirements
Business, Technical, and Non-Functional
Data Quality and Certification
Classify and Prioritize
Breadth over depth to identify possibilities
Manage scope, iterate, and evolve
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8. Requirements - Validation and Acceptance
Traceability of Requirements
To Design and Code
To Testing
Data Quality and Certification
Audit and Balancing Framework
Validate and Quality Certification Framework
Acceptance and Success Criteria
Defined by the Business Community
Validated through User Acceptance Testing
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9. Communications Engage and Partner The Retrospective
Collaboration with the business user community is needed, ranging from prioritizing the right thing to be working on to clarifying data quality, access, and usage.
Engage and Partner 1
The Retrospective
“When the trust account is high, communication is easy, instant, and effective.” Stephen R. Covey 
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10. Communications – Engage and Partner
With the Business Organization
Working with Business Stewards and SMEs
Leveraging Governance Teams and Committees
Status Meetings and Escalations
For status updates and transparency
For decision making and directional guidance
Fit Within the Data Ecosystem
Alignment to data access and availability plans
Alignment to data strategy and roadmaps
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11. Communications – The Retrospective
Retrospectives conducted at selected intervals are used to:
Recognize successes
Reflect
Obtain feedback
Actionable improvements are identified for adoption going forward.
Areas of Focus
Worked Well?
Not So Well?
Improvements
Update Best Practices
Update Approach
Data lake reflection
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12. Proof of Concept and Pilot
Proving It Out
Embedding Prove It and Show Me techniques within the development lifecycle provides more flexibility and ensures early business feedback.
Proof of Concept and Pilot 1
User Acceptance Test
“There’s two possible outcomes: if the result confirms the hypothesis, then you've made a discovery. If the result is contrary to the hypothesis, then you've made a discovery.” Enrico Fermi 
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13. Proving It Out – Proof of Concept and Pilot
Unknowns OR Well Defined Scope?
POC for discovery and emerging toolset
Pilot for fixed scope and anticipated delivery
Impact Design OR Build?
POC findings provide inputs to design
Pilot acts as Phase 1 of a framework build out
Findings OR Patterns to Reference?
POC findings answer How To questions
Pilot provides reference patterns and templates
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14. Proving It Out – User Acceptance Test
Embed into Development Framework
Works well for modular design
Occurs after several sprints are completed
Multiple Iterations
Increasing levels of detail per iteration
To provide data to users early and often
Ideal Fit for Agile and Iterative Methods
Aligns well with more frequent test cycles
Provides feedback to development
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15. Controlled Self-Service Business Intelligence
Controlled means providing reliable, consistent, and secure data for analytics and reporting, with enough data preparation to ensure trust.
Prepare and Enable 1
Control and Lend Order
“While the self-service aspect of modern BI platforms offers more freedom and power to business users, it doesn’t remove the responsibility of organizations to manage and maintain these analytics systems over time.” Brent Dykes 
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16. Controlled Self-Service BI – Prepare and Enable
Data Preparation
Cleanse, integrate, and organize
Profile, aggregate, and summarize
Tools and Their Support
Multiple tools (including familiar ones)
Tool Coach and Helper as an assistant
Metadata
Inventory Catalog of data lake contents
Metadata Repository for dictionary and lineage
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17. Controlled Self-Service BI – Control and Lend Order
Data Standardization
Business rules for data consistency
Defined, vetted, and pre-built components
Data Quality Management
Quality rules to define measurement
Rules execution and publishing of results
Data Governance Partnership
Business resources for rule definition
Technology resources for configuration
Data Lineage for data moving through zones
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18. Summary
Leverage requirements validation techniques to align to business goals
Frequent status and check in communications show transparency and establish trust
Use Prove It and Show Me approaches to deliver more flexibility and earlier feedback
Controlled self service depends on business collaboration and data management to be successful
Align to Goals
Establish Trust
Early Feedback
Business Partners
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19. Conclusion
Applying data management disciplines and best practices to the data lake build out increases and enhances its effectiveness as an integral component of the data ecosystem.
Collaborating with business data resources is essential to a successful implementation journey.
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20. Susan Swanson
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